Maybe it's because I'm obsessed with the topic, but it feels like everyone in AI is suddenly talking about verification 🤓. Not all verification is the same, though. Most of what labs ship today is probabilistic: agents cross-checking each other, one model grading another's work. It scales to fuzzy, human problems, and honestly, it's the only thing that does. But you get confidence levels, never guarantees. At @AIPredictable, we're betting on deterministic verification. Mathematical methods that give you a binary answer: does the code do what the spec says? Yes or no. We generate specs, translate them into Lean, and formally verify every possible theorem about expected behavior. Not just the happy path. It doesn't apply to everything. Ask it a philosophical question, and it's useless. But for code in banking, pharma, or healthcare, where "probably correct" doesn't cut it, the game changes. So, honest question: is probabilistic verification enough for what you're building, or do you need the math?

The last article of the #AICodePainPoints series is done! (For now, because every improvement in this field brings new issues with it.) This time, it's about what happens when a developer clicks Merge on AI-generated code reviewed by another AI. What are they actually approving? And in regulated industries, who carries the weight when something breaks? The accountability didn't disappear. It just concentrated in one click.

If you are curious about knowing the different pain points we see in the ai generated code, and how we think we can help, this series of articles could be interesting to you 😊

I'm lucky to be part of a founding team with people in tech for the second time in my life. 15 years later, the important parts are still the same. But the context has changed, and it says a lot about the times. I wrote about what it's like being part of @aipredictable : jorgegalindo.me/en/blog/post…

¡Vamos allá otra vez! Ilusionado como la primera vez. Hapiness is only real when shared. ❤️